library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(ComplexHeatmap)
library(grid)
coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
pcut <- params$pcut
# p1 <- ggplot(data.frame(x = 1:5, y = 1:5), aes(x, y)) + geom_point()
# vp_outer <- viewport(x = 0.5, y = 0.5, width = 0.5, height = 0.5, angle = 90)
# vp_inner <- viewport(x = 0.5, y = 0.5, width = 1, height = 1, angle = 0)
# grid.newpage()
# pushViewport(vp_outer)
# grid.rect()
# print(p1, vp = vp_inner)
# popViewport(1)
# g1 <- grid.grab()
# ggdraw() + draw_grob(g1)
## load global vars:
source("_src/global_vars.R")
# meta_tbl
# clrs
# markers_v7
# markers_v7_super
# cell_type_super_lookup
myfeatures <- c("UMAP_1", "UMAP_2", "umapharmony_1", "umapharmony_2", "sample", "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score", "cell_type", "cluster_label_sub")
seu_obj_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/T.super_processed_filtered_annotated.rds")
marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/T.super_marker_table_annotated_full.tsv")
marker_tbl_top <- marker_tbl %>%
filter(avg_logFC > 0.5,
p_val_adj < 0.01,
pct.1 > 0.2,
pct.2 < 0.8,
!is.na(cluster_label_sub),
!str_detect(gene, "^RPS|^RPL")) %>%
group_by(cluster_label_sub) %>%
slice(1:50)
marker_sheet <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/T.super_marker_sheet_full.tsv")
my_subtypes <- names(clrs$cluster_label_sub[[coi]])
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(sort_short = str_remove_all(sort_parameters, "singlet, live, ")) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal", "CD45-", sort_short))) %>%
mutate(cluster_label_sub = ordered(cluster_label_sub, levels = names(clrs$cluster_label_sub[[coi]])))
plot_data_sub <- filter(plot_data_sub, sort_short_x == cell_sort, !is.na(tumor_supersite))
UMAP
alpha_lvl <- ifelse(nrow(plot_data_sub) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data_sub) < 20000, 0.2, 0.05)
common_layers_disc <- list(
ggrastr::geom_point_rast(size = pt_size, alpha = alpha_lvl, raster.dpi = 150),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = ""),
theme(aspect.ratio = 1)
)
common_layers_cont <- list(
ggrastr::geom_point_rast(size = pt_size, alpha = alpha_lvl, raster.dpi = 150),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
umap_coord_anno <- function(size) {
ggplot(tibble(group = c("UMAP1", "UMAP2"),
x = c(0, 0), xend = c(1, 0),
y = c(0, 0), yend = c(0, 1),
lx = c(0.5, -0.15), ly = c(-0.15, 0.5),
angle = c(0, 90))) +
geom_segment(aes(x, y, xend = xend, yend = yend, group = group),
arrow = arrow(angle = 20, type = "closed", length = unit(0.1, "npc")),
size = size/4, lineend = "round") +
geom_text(aes(lx, ly, label = group, angle = angle), size = size) +
theme_void() +
coord_fixed(xlim = c(-0.3, 1), ylim = c(-0.3, 1))
}
add_umap_coord <- function(gg_obj, x = 0, y = 0, width = 0.3, height = 0.3, size = 4.5) {
p <- ggdraw() +
draw_plot(gg_obj, x = 0, y = 0, width = 1, height = 1) +
draw_plot(umap_coord_anno(size = size), x = x, y = y, width = width, height = height)
return(p)
}
plot_data_label <- plot_data_sub %>%
group_by(cluster_label_sub) %>%
summarise(umapharmony_1 = median(umapharmony_1),
umapharmony_2 = median(umapharmony_2)) %>%
mutate(cluster_number = as.numeric(cluster_label_sub))
umap_cell_type <- ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label_sub)) +
common_layers_disc +
#facet_wrap(~cluster_label_sub) +
geom_point(aes(umapharmony_1, umapharmony_2), color = "white",
data = plot_data_label, alpha = 0.5, size = 6) +
geom_text(aes(umapharmony_1, umapharmony_2, label = cluster_number), color = "black",
data = plot_data_label) +
scale_color_manual(values = clrs$cluster_label_sub[[coi]], labels = str_remove_all(names(clrs$cluster_label_sub[[coi]]), "functional|ivated")[-4]) +
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1), nrow = 12))
umap_site <- ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
common_layers_disc +
scale_color_manual(values = clrs$tumor_supersite)
umap_cell_type_void <- umap_cell_type + guides(color = F)
umap_site_void <- umap_site + guides(color = F)
umap_cell_type_legend <- cowplot::get_legend(umap_cell_type)
umap_site_legend <- cowplot::get_legend(umap_site)
module heatmap
module_names <- grep("pathway|CD8", colnames(seu_obj_sub@meta.data), value = T)
plot_data_modules <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, module_names))) %>%
gather(pathway, value, -c(1:length(myfeatures))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(sort_short = str_remove_all(sort_parameters, "singlet, live, ")) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal", "CD45-", sort_short))) %>%
mutate(cluster_label_sub = ordered(cluster_label_sub, levels = names(clrs$cluster_label_sub[[coi]]))) %>%
mutate(key_group = ifelse(str_detect(pathway, "pathway"), "pathway", "module")) %>%
mutate(pathway = str_remove_all(pathway, ".pathway|.module")) %>%
mutate(cluster_label_super = case_when(
str_detect(cluster_label_sub, "^CD4") ~ "CD4",
str_detect(cluster_label_sub, "^CD8") ~ "CD8",
str_detect(cluster_label_sub, "^NK") ~ "NK",
str_detect(cluster_label_sub, "^Cycling") ~ "Cycling",
T ~ "Other"
)) %>%
mutate(cluster_label_super = ordered(cluster_label_super, levels = c("CD4", "CD8", "Other", "NK", "Cycling")))
plot_data_modules <- filter(plot_data_modules, sort_short_x == cell_sort, !is.na(tumor_supersite))
plot_data_modules_summary_sample <- plot_data_modules %>%
group_by(pathway, key_group, sample, patient_id_short, cluster_label_sub, cluster_label_super) %>%
summarise(mean_score = mean(value, na.rm = T)) %>%
ungroup
plot_data_modules_summary_cluster <- plot_data_modules %>%
group_by(pathway, key_group, cluster_label_sub, cluster_label_super) %>%
summarise(mean_score = mean(value, na.rm = T)) %>%
ungroup %>%
mutate(cluster_number = as.numeric(cluster_label_sub))
common_heat_layers <- list(
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red"),
na.value = "grey10",
# breaks = c(-cut_value, 0, cut_value),
# labels = c(paste0("≤-", cut_value), "0", paste0("≥", cut_value)),
# limits = c(-cut_value, cut_value)
),
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.title = element_blank(),
plot.margin = margin(0, 0, 0, 0),
strip.text = element_blank())
)
pw_plot1 <- plot_data_modules_summary_cluster %>%
filter(key_group == "pathway") %>%
ggplot() +
geom_tile(aes(cluster_label_sub, pathway, fill = mean_score)) +
common_heat_layers +
facet_grid(~cluster_label_super, scales = "free", space = "free") +
labs(x = "Cluster", y = "Pathway", fill = "Mean\nPROGENy\nscore") +
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red"),
# labels = c(paste0("≤-", cut_value), "0", paste0("≥", cut_value)),
# breaks = c(-cut_value, 0, cut_value),
na.value = "grey10") +
theme(axis.text.x = element_blank())
pw_plot2 <- plot_data_modules_summary_cluster %>%
filter(key_group == "module") %>%
ggplot() +
geom_tile(aes(cluster_label_sub, pathway, fill = mean_score)) +
common_heat_layers +
facet_grid(~cluster_label_super, scales = "free", space = "free") +
labs(x = "Cluster", y = "Module", fill = "\n\n\nMean\nmodule\nscore") +
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red"),
# labels = c(paste0("≤-", cut_value), "0", paste0("≥", cut_value)),
# breaks = c(-cut_value, 0, cut_value),
na.value = "grey10") +
theme(axis.text.x = element_blank())
pw_plot1_anno <- plot_data_modules_summary_cluster %>%
distinct(cluster_label_sub, cluster_number, cluster_label_super) %>%
mutate(facet_helper = "") %>%
ggplot() +
geom_tile(aes(cluster_label_sub, facet_helper, fill = cluster_label_sub)) +
geom_point(aes(cluster_label_sub, facet_helper), color = "white", size = 6, alpha = 0.5) +
geom_text(aes(cluster_label_sub, facet_helper, label = cluster_number)) +
common_heat_layers +
scale_fill_manual(values = clrs$cluster_label_sub[[coi]]) +
facet_grid(~cluster_label_super, scales = "free", space = "free") +
theme(axis.text.y = element_blank(),
strip.text = element_blank()) +
guides(fill = F)
heat_module_grid <- plot_grid(pw_plot1, pw_plot2, pw_plot1_anno, rel_heights = c(0.45, 0.15, 0.4), ncol = 1, align = "v", axis = "lrtb")
heat_module_grid
ggsave("_fig/004_T_cell/004_module_heatmap.pdf", heat_module_grid, width = 12, height = 6.5)
marker heatmap
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, unique(marker_tbl_top$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(cluster_label_sub = ordered(cluster_label_sub, levels = my_subtypes)) %>%
group_by(cluster_label_sub, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(select(marker_tbl_top, cluster_label_sub_x = cluster_label_sub, gene), by = "gene") %>%
mutate(cluster_label_sub_x = ordered(cluster_label_sub_x, levels = rev(names(clrs$cluster_label_sub[[coi]])))) %>%
na.omit()
highlight_genes <- marker_tbl_top %>%
group_by(cluster_label_sub) %>%
slice(1:2) %>%
mutate(cluster_label_sub_x = ordered(cluster_label_sub, levels = rev(names(clrs$cluster_label_sub[[coi]])))) %>%
ungroup() %>%
select(cluster_label_sub_x, gene) %>%
na.omit %>%
mutate(highlight = T)
plot_data_markers_mat <- plot_data_markers %>%
spread(cluster_label_sub, value) %>%
left_join(highlight_genes, by = c("gene", "cluster_label_sub_x")) %>%
arrange(desc(cluster_label_sub_x), gene) %>%
select(cluster_label_sub_x, gene, highlight, everything())
ha_row <- rowAnnotation(
`Cell type` = plot_data_markers_mat$cluster_label_sub_x,
col = list(`Cell type` = clrs$cluster_label_sub[[coi]]),
show_legend = F,
annotation_name_side = "bottom"
)
ha_col <- columnAnnotation(
`Cell type` = anno_block(gp = gpar(fill = clrs$cluster_label_sub[[coi]], col = NA),
labels = as.character(1:(ncol(plot_data_markers_mat)-3)),
labels_rot = 90),
show_legend = F,
annotation_name_side = "left",
annotation_name_rot = 0
)
gene_idx <- plot_data_markers_mat$highlight == TRUE
ha_genes <- rowAnnotation(
link = anno_mark(
at = which(gene_idx),
labels = plot_data_markers_mat$gene[which(gene_idx)],
labels_gp = gpar(fontsize = 10), padding = unit(1, "mm"),
side = "left",
labels_rot = 180
)
)
marker_heatmap <- Heatmap(
as.matrix(plot_data_markers_mat[,-c(1:3)]),
heatmap_legend_param = list(
title = "Scaled expression",
title_position = "leftcenter-rot"
),
row_order = 1:length(plot_data_markers_mat$cluster_label_sub_x),
row_split = plot_data_markers_mat$cluster_label_sub_x,
column_split = 1:length(colnames(plot_data_markers_mat)[-c(1:3)]),
column_order = colnames(plot_data_markers_mat)[-c(1:3)],
column_names_side = "bottom",
right_annotation = ha_row,
left_annotation = ha_genes,
bottom_annotation = ha_col,
cluster_rows = F,
row_title = NULL,
column_title = NULL,
col = viridis(9)
)
# marker_heatmap
composition
per site
source("_src/comp_plot.R")
comp_tbl_sample_sort <- plot_data_sub %>%
filter(therapy == "pre-Rx", consensus_signature != "Undetermined") %>%
group_by(sample, tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, cluster_label_sub) %>%
tally %>%
group_by(sample, tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100,
log10n = log10(n)) %>%
mutate(sample_id = sample) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
ungroup %>%
mutate(cell_type_naive = ifelse(str_detect(cluster_label_sub, "mem"), "T memory", "other")) %>%
mutate(cell_type_dysfunc = ifelse(str_detect(cluster_label_sub, "dysfunc"), "T dysfunctional", "other"))
plist1 <- default_comp_grid_list(
comp_tbl_sample_sort, cell_type_naive, "T memory",
cluster_label_sub, super_type_sub = "T.super", vec_plot = T, nmax = 5000)
plist2 <- default_comp_grid_list(
comp_tbl_sample_sort, cell_type_dysfunc, "T dysfunctional",
cluster_label_sub, super_type_sub = "T.super", vec_plot = T, nmax = 5000)
comp_grid1 <- plot_grid(plotlist = plist1, ncol = 1, align = "v", rel_heights = c(0.1, 0.1, 0.15, 0.2, 0.45))
comp_grid2 <- plot_grid(plotlist = plist2, ncol = 1, align = "v", rel_heights = c(0.1, 0.1, 0.15, 0.2, 0.45))
comp_grid1
comp_grid2
ggsave("_fig/004_T_cell/004_T.cell_comp_memory.pdf", comp_grid1, width = 3, height = 9)
ggsave("_fig/004_T_cell/004_T.cell_comp_dysfunc.pdf", comp_grid2, width = 3, height = 9)
heatmap_grob <- grid.grabExpr(draw(marker_heatmap), width = 8, height = 16)
vp_outer <- viewport(x = 0.5, y = 0.5, width = 0.5, height = 2, angle = 270)
vp_inner <- viewport(x = 0.5, y = 0.5, width = 1, height = 1, angle = 0)
grid.newpage()
pushViewport(vp_outer)
grid.draw(heatmap_grob)
popViewport(1)
heatmap_grob_rot <- grid.grab()
umap_heat_grid <- ggdraw() +
draw_plot(add_umap_coord(umap_cell_type_void, size = 3.5, x = 0),
x = 0, y = 0.5, width = 0.2, height = 0.5) +
draw_plot(add_umap_coord(umap_site_void, size = 3.5, x = 0),
x = 0, y = 0, width = 0.2, height = 0.5) +
draw_grob(heatmap_grob_rot,
x = 0.2, y = 0, width = 0.8, height = 1)
# draw_plot_label(c("A", "B", "C", "D"), x = c(0, 0.63, 0, 0), y = c(0.995, 0.995, 0.72, 0.45))
umap_heat_grid
ggsave("_fig/004_T_cell/004_T.cell_umap_heat.png", umap_heat_grid, width = 20, height = 8)
ggsave("_fig/004_T_cell/004_T.cell_umap_heat.pdf", umap_heat_grid, width = 20, height = 8)
session info
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-03-09
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## beeswarm 0.2.3 2016-04-25 [2]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## bitops 1.0-6 2013-08-17 [2]
## broom 0.7.2 2020-10-20 [1]
## Cairo 1.5-12.2 2020-07-07 [1]
## callr 3.4.2 2020-02-12 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## circlize 0.4.10 2020-06-15 [1]
## cli 2.0.2 2020-02-28 [1]
## clue 0.3-57 2019-02-25 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## ComplexHeatmap * 2.2.0 2019-10-29 [1]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## desc 1.2.0 2018-05-01 [2]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## GetoptLong 1.0.2 2020-07-06 [1]
## ggbeeswarm 0.6.0 2017-08-07 [2]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggrastr 0.1.9 2020-06-20 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## GlobalOptions 0.1.2 2020-06-10 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.5.1.1 2021-01-22 [1]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## pbapply 1.4-2 2019-08-31 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppParallel 4.4.4 2019-09-27 [2]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rjson 0.2.20 2018-06-08 [1]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## shape 1.4.4 2018-02-07 [1]
## sn 1.5-4 2019-05-14 [2]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vctrs 0.3.5 2020-11-17 [1]
## vipor 0.4.5 2017-03-22 [2]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## yaml 2.2.1 2020-02-01 [1]
## zoo 1.8-7 2020-01-10 [2]
## source
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## Github (clauswilke/colorblindr@1ac3d4d)
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##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library